Cellular wireless has enjoyed an extremely rapid growth from the 1980s, and there is now an almost widespread coverage of cellular radio services in industrialised countries. In recent years there has been a similar, albeit more rapid, growth in the demand and supply of data services in a wireless environment. Such data services include Internet and intranet traffic and other (principally packet-based) data transmission schemes. Indeed, demand for the support of data services can be seen by the amount of e-mail retrieval arid web browsing that already takes place in locations such as airport lounges, hotel lobbies, company conference rooms and dedicated rooms with ports for connection to electronic notebooks and lap top computers.
As will be appreciated, data traffic is both asymmetric in nature and is not time critical; this contrasts with a generally uniform bandwidth distribution in voice traffic and the requirement that voice traffic be subjected to a maximum latency for coherent information reception. More explicitly, far greater data bandwidth is necessary for a wireless access point to subscriber terminal (i.e. the so-called forward link or down link) than that bandwidth required in the reverse link or uplink direction. The asymmetry in data communication, as will be understood, arises from the fact that the uplink is generally used to solicit information (e.g. web pages) as opposed to relaying data files.
Cellular subscriber handsets, whilst fundamentally providing mobility, are nevertheless used when effectively stationary which may subject the subscriber handset (or the like) to fading conditions associated with the channel. Facing is a dominant process that adversely affects quality of service experienced in second generation cellular systems, such as the frequency division multiplexed (FDM) global system for mobile communication (GSM). Such second generation systems often operate in a time division multiplexed (TDM) downlink transmission mode to provide time diversity which attempts to address fading. Code division multiple access (CDMA) systems, however, are inherently less susceptible to channel and physical environments because spread spectrum transmissions provide diversity gain to mitigate adverse multipath effects like fading, with each channel in the CDMA system defined by a unique spreading code on a frequency carrier. Information recovery from an assigned channel resource in a CDMA-based system is therefore dependent upon knowledge of the spreading code. More specifically, each channel is comprised from a unique coded sequence of “chips” that are selected from a relatively long pseudo-random spreading sequence (typically many millions of bits in length). A communication device has access to an information-bearing channel by virtue of a communication device having particular and detailed knowledge of a specific code that identifies the specific bits used by the information-bearing channel.
The use of orthogonal Walsh codes for downlink transmit diversity antennas may be used to address fading and coloring of a channel through use of space-time transmit diversity (STTD) for non-dispersive channels; this is described in the paper “Space Time Block Coded Transmit Antenna Diversity for WCDMA” presented to the European Technical Standards Institute (ETSI) on 30 Oct. 1998 by Texas Instruments. Essentially, STTD provides a linear algebraic way of resolving out symbols in a channel through an interleaving mechanism of CDMA code words and, more particularly, the use of inverted and complex conjugated versions of interleaved CDMA code words in a diversity path. In STTD, the length of the CDMA code words support a certain capability for inter-symbol rejection.
Information (such as voice, data or video) is spread across all chips of the spreading sequences, with the processing gain of the system determined by the number of chips required to construct a single data bit. Essentially, the processing gain is a ratio defined by the number of chips required per symbol/bit (generally fixed for a second generation network). In general, it is therefore better that a receiver has a high processing gain in order that it is better able to distinguish each user signal against a background of other-user generated interference and system noise.
From a practical but exemplary perspective, a TDD-CDMA-based system may exhibit a frame structure of duration ten milliseconds (10 ms). Each frame will contain a number of slots or packets, say fifteen in total, of which at least one is usually assigned to function as a control channel. Dependent upon a spreading factor of the CDMA-based system (but assuming that the frame is indeed 10 ms in duration and fifteen slots in length), each packet or slot (of duration 667 μs) is 2560 chips in length. Each packet or slot further contains at least one (and usually at least two) data portion(s) and at least one training sequence interspersed between successive data portions. The training sequence always contains a relatively high number of chips (e.g. five hundred and twelve in one particular form) that can be contrasted with the relatively few chips assigned to a data symbol. The training sequence, which in essence is simply a burst of random data known to an addressed device, is therefore used to assess (by virtue of a cross-correlation function) a channel impulse response for channel equalisation purposes, with the length of the training sequence rendering it effectively immune to degradation in the downlink path. In a particular system, a CDMA code word, sometimes referred to as a “user symbol”, comprises a maximum of 256 chips. Consequently, each slot or packet of each frame contains a total of at least ten symbols and, possibly, hundreds of symbols with 512 chips (i.e. two CDMA code words, for example) consumed as the training sequence. Putting this in a slightly different light, the 2048 remaining chips in a slot (2560–512 chips of the training sequence) comprise a number of CDMA code words based upon an applied (and varying) spreading factor. The spreading factor (SF) typically varies between, say, one and two hundred and fifty-six, with a spreading factor of sixteen therefore yielding one hundred and twenty-eight CDMA code words.
A high speed downlink Internet access system for nomadic users within a CDMA physical layer architecture is described in the paper “CDMA/HDR: A Bandwidth Efficient High-Speed Wireless Data Service for Nomadic Users” by Paul Bender et al, Pages 70 to 77, IEEE Communications Magazine, July 2000.
The use of equalisers (as opposed to RAKE receivers) in communication devices, e.g. handsets and the like, is commonly advocated for third generation applications since it enhances bit error rate (BER) performance and can increase spectral efficiency. The function of the equaliser is to mitigate symbol corruption induced by an imperfect channel, with an equaliser designed to condition data such that it appears to have been passed through, optimally, a perfect channel.
Equaliser design and particularly equaliser efficiency are presently constrained by processing overhead/workload. In the context of a mobile device, workload must necessarily be restricted because of its associated drain on battery power. Unfortunately, in a dispersive multi-path channel, finite impulse response (FIR) equalisers require a significant number of filter “taps”; with each tap representing a different weighting coefficient that is applied to a sample. Increasing the number of taps improves accuracy and effectiveness of the FIR equaliser. A significant problem occurs with implementation of FIR filters in equalisers, namely that the each sample applied to an input of the FIR equaliser must be multiplexed by each tap. In a typical CDMA-type system, by way of example, there are eight samples per chip and a chip rate typically in excess of 1 MHz; this results in significant workload for a digital signal processor. Indeed, in a bad time dispersive multi-path channel environment, a seventy-tap FIR could be required. A truncation of the number of taps would reduce computation load but at the cost of introducing side lobes into any frequency domain representation. Furthermore, if the sample rate of each tap is increased by N-fold in order to provide a requisite degree of over-sampling, the number of taps required to span a certain time interval and maintain the frequency response also increases by N-fold. Consequently, there is a resultant N2-fold increase in computation load. Thus, equalisers containing filters designed using straightforward FIR equalisation can be very expensive.
With respect to equalisers and mechanism of equalisation, these generally fall into one of several categories identified immediately below:
(i) Cholesky factorisation of the channel impulse response autocovariance matrix. This factorisation, which relies on matrix manipulation, is efficient when the autocovariance matrix is strongly banded, but this banding is only associated with low dispersion channels. After matrix factorisation, least squares filter equations are solved by back substitution, as usual in the Cholesky method, although the processing load is dependent upon the duration of the multipath spread. The Cholesky factorisation method can also be ill-conditioned and numerically unstable and some measure of accuracy in the computation is required;
(ii) Decision feedback equalisers (DFE). These uses a combination of forward FIR filtering, a threshold decision device (such as a hard limiter) and a feedback filter. Essentially, the feedback loop provides a mechanism that attempts to subtract echos by feeding back the channel impulse response (CIR). However, the channel subtraction mechanism provides error propagation problems. DFEs are commonly used in FDMA applications, such as the US 2nd generation cellular phone receivers and telephone modem equalisers.
(iii) Zero-forcing filters synthesise an FIR filter and operate to equalise channel dispersion for a finite time span about an origin. For an n-tap FIR Filter, solving to a set of linear equations will always form a weight set such that the convolution of the sample channel impulse response Hk and the Wiener filter response Wk will have a combined impulse response which is zero at n−1 arbitrary points and has a central unit response at k=0; and
(v) The Wiener least squares filter. This equalisation technique utilises a modified inverse filter which controls the white noise response of the filter, ie. the undesired enhancement of thermal noise from the antenna. If the discrete frequency response of the channel is Hk and the thermal noise variance is σ2, then the Wiener filter frequency response is:
      W    k    =            H      k      *                                                      H            k                                    2            +              σ        2            
The paper “Smart Antennas for Third Generation Mobile Radio Systems” by Martin Haardt (Siemens), Stanford Colloquium on Smart Antennas”, July 1999, describes channel equalisation in terms of the Wiener filtering response, and explores how two or more receiving antennas can be included in the description of the received data. A further paper by H Sari et al titled “Transmission Techniques for Digital TV Broadcasting”, IEEE communications magazine 33(2) February 1995, discusses channel equalisation in the context of the Wiener filter mechanism. STTD is included in the standards for 3G cellular systems in the European UMTS Terrestrial Radio Access (UTRA) system and similar systems in the American CDMA2000 proposals. Consequently, it is important for these STTD systems efficiently to implement data recovery and equalisation algorithms in battery powered mobile units.
In general, least squares solutions (such as (i) and (iv) above) are better than algebraic solutions and give better BER performance.
In a dispersive channel for space time transmit diversity (STTD), the channel impulse response matrix, data vector and antenna signals can be written in block vector form, with the antenna signals related to the CIR and data vector by a block convolution operation:
                    y                  2          ⁢          k                    =                                    (                                                                                Y                    0                                                                                                                    Y                    2                                                                                                                    Y                    4                                                                                                ⋮                                                      )                    ⁢                                          ⁢                      d                          2              ⁢              k                                      =                                            (                                                                                          D                      0                                                                                                                                  D                      2                                                                                                                                  D                      4                                                                                                            ⋮                                                              )                        ⁢                                                  ⁢                          h                              2                ⁢                k                                              =                      (                                                                                H                    0                                                                                                                    H                    2                                                                                                                    H                    4                                                                                                ⋮                                                      )                                ⁢                            y              2        ⁢        k              =                  ∑                  τ          =          0                          2          ⁢          L                    ⁢                        d                      2            ⁢                          (                              k                -                τ                            )                                      ⁢                  h                      2            ⁢                                                  ⁢            τ                              
The STTD operation results in the associated equaliser having to run at half the rate of the channel sampler; this compromises the capabilities of the equaliser to an extent that renders the performance of the equaliser worse than an equivalent equaliser without STTD. Furthermore, the performance of RAKE filters, when installed into the equaliser, is very poor in any significant multi-path.
Channel equalisation to address channel imperfections therefore poses implementation difficulties.
Joint user detection algorithms (JDAs) are usually reserved for CDMA systems that use low spreading factors (e.g. less than 32). JDA is an algebraic solution in the receiver that counters inter-user interference caused by multi-path in the radio channel, typically by using matrix algebra and linear equations. The multipath renders Walsh codes belonging to different users non-orthogonal to an extent dependent on amplitude severity. For this reason, JDA is of interest in high-speed data options of, for example, 3G standards where spreading factors down to four may be encountered at the highest data rates. It has been proposed that all available Walsh codes should be used in parallel for each user on the downlink for an asymmetric bandwidth “fat pipe” variant of the 3G standards, and under these conditions the channel equaliser or JDA is desirable. Unfortunately, a significant issue arises from that fact that JDAs are inherently processor-intensive operations and that JDAs are required in a handheld portable terminals in which battery drain is roughly proportional to the instruction processing rate of the processor and hence to the complexity of the algorithms. As such, there is a fundamental conflict between data recovery and effective mobile operation, with it being desirable to attain a low DSP computation count in order to extend battery life.